Abstract: Characterizing quantum systems through experimental data is critical to
applications as diverse as metrology and quantum computing. Analyzing this
experimental data in a robust and reproducible manner is made challenging,
however, by the lack of readily-available software for performing principled
statistical analysis. We improve the robustness and reproducibility of
characterization by introducing an open-source library, QInfer, to address this
need. Our library makes it easy to analyze data from tomography, randomized
benchmarking, and Hamiltonian learning experiments either in post-processing,
or online as data is acquired. QInfer also provides functionality for
predicting the performance of proposed experimental protocols from simulated
runs. By delivering easy-to-use characterization tools based on principled
statistical analysis, QInfer helps address many outstanding challenges facing
quantum technology.